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Collaborative filtering recommendation method, server and storage medium

A collaborative filtering recommendation, multi-layer perceptron technology, applied in neural learning methods, instruments, network data retrieval and other directions, can solve the problem of not fully utilizing user-item rating information, etc., to solve collaborative filtering recommendation problems, easy to solve, The effect of enhancing expressiveness

Pending Publication Date: 2022-06-21
WUHAN UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the NCF method does not make full use of the user-item rating information

Method used

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  • Collaborative filtering recommendation method, server and storage medium
  • Collaborative filtering recommendation method, server and storage medium
  • Collaborative filtering recommendation method, server and storage medium

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Embodiment Construction

[0045] Reference will now be made in detail to specific embodiments of the present invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with specific embodiments, it will be understood that the intention is not to limit the invention to the described embodiments. On the contrary, the intention is to cover changes, modifications and equivalents included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein may all be implemented by any functional block or arrangement of functions, and that any functional block or arrangement of functions may be implemented as physical or logical entities, or a combination of both.

[0046] In order to make those skilled in the art better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specifi...

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Abstract

The invention discloses a collaborative filtering recommendation method, a server and a storage medium, and relates to the technical field of information recommendation. The method comprises the following steps: training a sample, and obtaining a user and item vector set; encoding and decoding the vector by using an automatic encoder to obtain a potential representation set; calculating a product phi 1 of potentially represented elements; cascading the user-project potential representation as the input of the multilayer sensor, and calculating an output value phi 2; cascading phi 1 and phi 2, calculating a prediction score, constructing a loss function, and optimizing and solving model parameters; and iteratively repeating until the model converges, and outputting the collaborative filtering recommendation model. Compared with the prior art, the method has the advantages that the user-item scoring information is fully utilized, and better user-item representation is obtained. The method has the advantages of being high in fitness and easy to solve the optimization problem, the collaborative filtering recommendation problem can be effectively solved, and better recommendation performance is achieved.

Description

technical field [0001] The invention relates to the technical field of information recommendation, in particular to a collaborative filtering recommendation method based on dual autoencoders, a server and a storage medium. Background technique [0002] With the rapid development of the Internet, the amount of information on the Internet has increased significantly, so that users cannot obtain the part of the information that is really useful to them when faced with a large amount of information, resulting in an increasingly serious problem of information overload. Recommender systems play an important role in alleviating information overload. It recommends information and products that users are interested in based on their information needs, interests, etc., and has been widely used in many online services. Collaborative filtering is an effective method to achieve personalized recommendation. It generally uses the nearest neighbor technology, uses the user's historical pref...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/958G06K9/62G06N3/04G06N3/08
CPCG06F16/9536G06F16/958G06N3/084G06N3/048G06F18/214G06F18/241
Inventor 刘新宇杜博王增茂
Owner WUHAN UNIV
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